IDEAS home Printed from https://ideas.repec.org/a/spr/jcsosc/v8y2025i2d10.1007_s42001-025-00380-0.html
   My bibliography  Save this article

Quantifying collective attention and fan engagement: a case study of the Japanese professional baseball league

Author

Listed:
  • Naofumi Otomo

    (University of Tsukuba)

  • Kazutoshi Sasahara

    (Institute of Science Tokyo)

  • Makoto Mizuno

    (Meiji University)

  • Yukie Sano

    (University of Tsukuba)

Abstract

The rise of social media has led to new studies on collective attention in specific events such as elections and sports. In the context of collective attention, phenomena such as rapid increases in the number of posts and the sentiment of the content have been extensively studied. However, microscopic details, like who is participating and the specific words used in posts, are not yet fully understood. Therefore, this study proposes a new indicator to quantify the state where a broad range of participants exhibit their narrow attention. We tested this indicator using over 10 million tweets related to Japanese professional baseball teams, where many participants exhibit their attention towards the team they support. We confirmed that collective attention occurs in both positive events, such as championships, and negative events, such as player injuries. This did not necessarily correspond to the simple post volume. Additionally, an analysis distinguishing between own-team fans and fans of other teams revealed significant collective attention occurred when involving fans of other teams. By employing the microscopic perspective defined in this study, which considers localized attention with a broad range of participants, we elucidate the mechanisms of collective attention and provide insights into the origins of collective attention.

Suggested Citation

  • Naofumi Otomo & Kazutoshi Sasahara & Makoto Mizuno & Yukie Sano, 2025. "Quantifying collective attention and fan engagement: a case study of the Japanese professional baseball league," Journal of Computational Social Science, Springer, vol. 8(2), pages 1-15, May.
    • Handle: RePEc:spr:jcsosc:v:8:y:2025:i:2:d:10.1007_s42001-025-00380-0
    DOI: 10.1007/s42001-025-00380-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42001-025-00380-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s42001-025-00380-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Samuel López-Carril & Paloma Escamilla-Fajardo & Maria Huertas González-Serrano & Vanessa Ratten & Rómulo Jacobo González-García, 2020. "The Rise of Social Media in Sport: A Bibliometric Analysis," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 1-29, October.
    2. Chang, Yonghwan, 2019. "Spectators’ emotional responses in tweets during the Super Bowl 50 game," Sport Management Review, Elsevier, vol. 22(3), pages 348-362.
    3. Kazutoshi Sasahara, 2019. "You are what you eat," Journal of Computational Social Science, Springer, vol. 2(2), pages 103-117, July.
    4. Young-Ho Eom & Michelangelo Puliga & Jasmina Smailović & Igor Mozetič & Guido Caldarelli, 2015. "Twitter-Based Analysis of the Dynamics of Collective Attention to Political Parties," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-17, July.
    5. Kazutoshi Sasahara & Yoshito Hirata & Masashi Toyoda & Masaru Kitsuregawa & Kazuyuki Aihara, 2013. "Quantifying Collective Attention from Tweet Stream," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-10, April.
    6. Philipp Lorenz-Spreen & Bjarke Mørch Mønsted & Philipp Hövel & Sune Lehmann, 2019. "Accelerating dynamics of collective attention," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    7. Michael Szell & Sébastian Grauwin & Carlo Ratti, 2014. "Contraction of Online Response to Major Events," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-9, February.
    8. Yonghwan Chang, 2019. "Spectators’ emotional responses in tweets during the Super Bowl 50 game," Sport Management Review, Taylor & Francis Journals, vol. 22(3), pages 348-362, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tingting Nian & Yuheng Hu & Cheng Chen, 2021. "Examining the Impact of Television-Program-Induced Emotions on Online Word-of-Mouth Toward Television Advertising," Information Systems Research, INFORMS, vol. 32(2), pages 605-632, June.
    2. Darko Cherepnalkoski & Andreas Karpf & Igor Mozetič & Miha Grčar, 2016. "Cohesion and Coalition Formation in the European Parliament: Roll-Call Votes and Twitter Activities," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-27, November.
    3. Antonio, Fernando J. & Itami, Andreia S. & Dalmedico, Jônatas F. & Mendes, Renio S., 2022. "On the dynamics of reporting data: A case study of UFO sightings," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    4. Zhenpeng Li & Xijin Tang & Zhenjie Hong, 2022. "Collective attention dynamic induced by novelty decay," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(8), pages 1-11, August.
    5. Pardo Pintos, Alejandro & Shalom, Diego E. & Tagliazucchi, Enzo & Mindlin, Gabriel & Trevisan, Marcos, 2023. "A model of phase-coupled delay equations for the dynamics of word usage," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    6. Shota Saito & Yoshito Hirata & Kazutoshi Sasahara & Hideyuki Suzuki, 2015. "Tracking Time Evolution of Collective Attention Clusters in Twitter: Time Evolving Nonnegative Matrix Factorisation," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-17, September.
    7. Margherita Vestoso, 2018. "The GDPR beyond Privacy: Data-Driven Challenges for Social Scientists, Legislators and Policy-Makers," Future Internet, MDPI, vol. 10(7), pages 1-11, July.
    8. Liu, Jian-Guo & Yang, Zhen-Hua & Li, Sheng-Nan & Yu, Chang-Rui, 2018. "A generative model for the collective attention of the Chinese stock market investors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1175-1182.
    9. Lehrer, Steven & Xie, Tian & Zhang, Xinyu, 2021. "Social media sentiment, model uncertainty, and volatility forecasting," Economic Modelling, Elsevier, vol. 102(C).
    10. Ceyda Sanlı & Renaud Lambiotte, 2015. "Local Variation of Hashtag Spike Trains and Popularity in Twitter," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-18, July.
    11. Wentao Xu & Kazutoshi Sasahara, 2022. "Characterizing the roles of bots on Twitter during the COVID-19 infodemic," Journal of Computational Social Science, Springer, vol. 5(1), pages 591-609, May.
    12. Kelvin J. L. Koa & Yunshan Ma & Ritchie Ng & Huanhuan Zheng & Tat-Seng Chua, 2024. "Temporal Relational Reasoning of Large Language Models for Detecting Stock Portfolio Crashes," Papers 2410.17266, arXiv.org.
    13. David J. Grüning, 2022. "Synthesis of human and artificial intelligence: Review of “How to stay smart in a smart world: Why human intelligence still beats algorithms” by Gerd Gigerenzer," Futures & Foresight Science, John Wiley & Sons, vol. 4(3-4), September.
    14. Binbin Ye & Padmaja Krishnan & Shiguo Jia, 2022. "Public Concern about Air Pollution and Related Health Outcomes on Social Media in China: An Analysis of Data from Sina Weibo (Chinese Twitter) and Air Monitoring Stations," IJERPH, MDPI, vol. 19(23), pages 1-21, December.
    15. Yi Luo & Xiaoping Yang & Xiaoming Li & Zhenzhen Chen & Fangyuan Liu, 2024. "Human emergency behaviour and psychological stress characteristic mining based on large-scale emergencies," Computational and Mathematical Organization Theory, Springer, vol. 30(4), pages 293-320, December.
    16. Abu Bashar & Mohammad Wasiq & Brighton Nyagadza & Eugine Tafadzwa Maziriri, 2024. "Emerging trends in social media marketing: a retrospective review using data mining and bibliometric analysis," Future Business Journal, Springer, vol. 10(1), pages 1-16, December.
    17. Matilde Giaccherini & Joanna Kopinska & Gabriele Rovigatti, 2022. "Vax Populi: The Social Costs of Online Vaccine Skepticism," CESifo Working Paper Series 10184, CESifo.
    18. Leihan Zhang & Ke Xu & Jichang Zhao, 2017. "Sleeping beauties in meme diffusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 383-402, July.
    19. Chan, Ho Fai & Bodiuzzman, Sohel Md & Torgler, Benno, 2020. "The power of social cues in the battle for attention: Evidence from an online platform for scholarly commentary," Journal of Informetrics, Elsevier, vol. 14(4).
    20. Moena Hashimoto & Yotaro Takazawa & Kazutoshi Sasahara, 2024. "Are meat alternatives a moral concern? A comparison of English and Japanese tweets," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcsosc:v:8:y:2025:i:2:d:10.1007_s42001-025-00380-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.